Characterizing the three-dimensional structure of objects has fundamental value across many industries and areas of research. Applications include the analysis of existing materials, synthesizing new materials, and predictive correlations for how these materials perform under various conditions. For example the shape of a protein chain may contribute to its effectiveness as a drug, and understanding the shape may lead to similarly designed or improve drugs. The same goes for developing high performance materials in other fields.
Currently to characterize the morphology of an object or material one may use geometric properties such as length, angle, and curvature to define quantities to bring out the features of importance. For example, a cell has a diameter that may be defined in various ways such as the diameter of the smallest circumscribed or largest inscribed sphere. The volume of a cell is also defined by using the product of length differentials ∫ ∫ ∫ . . . dxdydz. These geometric properties are based on distance measurement and have value for conducting research and developing technologies into perhaps every branch of science.
Current geometrical methods used to categorize or analyze datasets are unable to extract or process morphological features that humans routinely take for granted. However, morphological features are also extremely important criteria along which data may be analyzed and categorized. Accordingly, there is an immediate need for computing systems that can generally categorize data based upon the morphological properties of the data.